{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "pycharm": {
          "name": "#%% md\n"
        }
      },
      "source": "### sns.heatmap\n* 离散数据的波动变化 \n* 使用颜色趋势表现变化"
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "outputs": [],
      "source": "# %matplotlib inline # 设置jupyter为IDE的时候,绘制的图显示在控制台中\nimport matplotlib as mat\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\n\nnp.random.seed(1) # 设置随机种子固定, 保持每一次的随机数是一致的\n\nsns.set()  # 使用默认的seaborn设置",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%%\n",
          "is_executing": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "### sns.heatmap() 参数\n*  heatmap(\n     * data, \n     * vmin\u003dNone, \n     * vmax\u003dNone, \n     * cmap\u003dNone, \n     * center\u003dNone, \n     * robust\u003dFalse,\n     * annot\u003dNone, \n     * fmt\u003d\".2g\", \n     * annot_kws\u003dNone,\n     * linewidths\u003d0, \n     * linecolor\u003d\"white\",\n     * cbar\u003dTrue, \n     * cbar_kws\u003dNone, cbar_ax\u003dNone,\n     * square\u003dFalse, \n     * xticklabels\u003d\"auto\", \n     * yticklabels\u003d\"auto\",\n     * mask\u003dNone, \n     * ax\u003dNone, \n     * **kwargs):\n    \n    Parameters\n    ----------\n    data : rectangular dataset\n        2D dataset that can be coerced into an ndarray. If a Pandas DataFrame\n        is provided, the index/column information will be used to label the\n        columns and rows.\n    vmin, vmax : floats, optional\n        Values to anchor the colormap, otherwise they are inferred from the\n        data and other keyword arguments.\n    cmap : matplotlib colormap name or object, or list of colors, optional\n        The mapping from data values to color space. If not provided, the\n        default will depend on whether ``center`` is set.\n    center : float, optional\n        The value at which to center the colormap when plotting divergant data.\n        Using this parameter will change the default ``cmap`` if none is\n        specified.\n    robust : bool, optional\n        If True and ``vmin`` or ``vmax`` are absent, the colormap range is\n        computed with robust quantiles instead of the extreme values.\n    annot : bool or rectangular dataset, optional\n        If True, write the data value in each cell. If an array-like with the\n        same shape as ``data``, then use this to annotate the heatmap instead\n        of the raw data.\n    fmt : string, optional\n        String formatting code to use when adding annotations.\n    annot_kws : dict of key, value mappings, optional\n        Keyword arguments for ``ax.text`` when ``annot`` is True.\n    linewidths : float, optional\n        Width of the lines that will divide each cell.\n    linecolor : color, optional\n        Color of the lines that will divide each cell.\n    cbar : boolean, optional\n        Whether to draw a colorbar.\n    cbar_kws : dict of key, value mappings, optional\n        Keyword arguments for `fig.colorbar`.\n    cbar_ax : matplotlib Axes, optional\n        Axes in which to draw the colorbar, otherwise take space from the\n        main Axes.\n    square : boolean, optional\n        If True, set the Axes aspect to \"equal\" so each cell will be\n        square-shaped.\n    xticklabels, yticklabels : \"auto\", bool, list-like, or int, optional\n        If True, plot the column names of the dataframe. If False, don\u0027t plot\n        the column names. If list-like, plot these alternate labels as the\n        xticklabels. If an integer, use the column names but plot only every\n        n label. If \"auto\", try to densely plot non-overlapping labels.\n    mask : boolean array or DataFrame, optional\n        If passed, data will not be shown in cells where ``mask`` is True.\n        Cells with missing values are automatically masked.\n    ax : matplotlib Axes, optional\n        Axes in which to draw the plot, otherwise use the currently-active\n        Axes.\n    kwargs : other keyword arguments\n        All other keyword arguments are passed to ``ax.pcolormesh``.\n\n",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%% md\n"
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "outputs": [
        {
          "name": "stdout",
          "text": [
            "[[4.17022005e-01 7.20324493e-01 1.14374817e-04]\n [3.02332573e-01 1.46755891e-01 9.23385948e-02]\n [1.86260211e-01 3.45560727e-01 3.96767474e-01]]\n"
          ],
          "output_type": "stream"
        },
        {
          "data": {
            "text/plain": "\u003cFigure size 432x288 with 2 Axes\u003e",
            "image/png": "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\u003d\n"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": "uniform_data \u003d np.random.rand(3 , 3)  # 生成 3x3 的矩阵数据\nprint(uniform_data)\nheatmap \u003d sns.heatmap(data\u003duniform_data)\n",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%%\n",
          "is_executing": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "### 设置右侧的调色板的区间取值\n* vmin 调色板最小值\n* vmax 调色板最大值\n* 可以用于区分最大值,最小值",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%% md\n"
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "outputs": [
        {
          "name": "stdout",
          "text": [
            "[[0.53881673 0.41919451 0.6852195  0.20445225]\n [0.87811744 0.02738759 0.67046751 0.4173048 ]\n [0.55868983 0.14038694 0.19810149 0.80074457]\n [0.96826158 0.31342418 0.69232262 0.87638915]]\n"
          ],
          "output_type": "stream"
        },
        {
          "data": {
            "text/plain": "\u003cmatplotlib.axes._subplots.AxesSubplot at 0x1cf485d65c0\u003e"
          },
          "metadata": {},
          "output_type": "execute_result",
          "execution_count": 16
        },
        {
          "data": {
            "text/plain": "\u003cFigure size 432x288 with 2 Axes\u003e",
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVoAAAD7CAYAAAArZlyJAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADkFJREFUeJzt3X+s3Xddx/HnvdvSar0FcYr+AY7F7S0JrEiqu90KUuMwXRyti/wBamBYxqIJjEVZ58BFYkwUu+Gc1bBmUyEEYVjdlqxTQwHX2aW64EpY33pnplhkOn70h6Pt7jnHP865eHLpPefc7nzu93u+9/kg36Tn+733c9/888p77+/n+z1TnU4HSVI501UXIElNZ9BKUmEGrSQVZtBKUmEGrSQVZtBKUmEGrSQtISIuj4jPnuX8NRFxKCL+ISLeOWwdg1aSziIi3gfsAdYuOn8BcAfwRuAngesj4gcHrWXQStLZPQVce5bzrwTmMvMbmXkGeAR43aCFzi9Q3Le9/CWv9rGznq+c/HrVJdTGh1+6peoSauPGZ/ZXXUJtzJ85OvVC13j+2X8bOXMuuPDigX8vMz8dERed5dJ64Fjf5xPAiwatZUcrSctzHJjp+zwDfHPQLxTtaCVpRbVbK/FXngQuiYiXACeB1wO/P+gXDFpJzdGaL7Z0RLwV+J7M/EhE3AQ8THcqcE9mHh30uwatpMbodNpjXS8znwZme//+eN/5B4AHRl3HoJXUHO3xBu24GLSSmmPMHe24GLSSmmNlboYtm0ErqTnsaCWprE7BXQcvhEErqTm8GSZJhTk6kKTCvBkmSYXZ0UpSYd4Mk6TCvBkmSWV1Os5oJaksZ7SSVJijA0kqzI5WkgprPV91BWdl0EpqDkcHklSYowNJKsyOVpIKq2nQTo/6gxEx8s9KUhU6redHPlbSwI42Ii4Gbgc2AvO9sD0MvDcz/2UF6pOk0U3ojHYPcEtmPrZwIiJmgXuBK0sWJknLNqGjg7X9IQuQmQcL1iNJ567THv1YQcM62n+OiHuAfcAxYAa4GniidGGStGw17WiHBe2vANuBzcB64DjwILC3cF2StHyTOKPNzA7dUDVYJdXfvC/+lqSyJrGjlaSJMqEzWkmaHHa0klSYHa0kFWZHK0mFuetAkgrrdKqu4KwMWknN4YxWkgozaCWpsDHdDOu9EnY3sAE4DezIzLm+6zcDb6H7WoLfy8wHB63ny7wlNUerNfox2Ha6by/cBOwEdi1ciIhXA28FZoE3Ah+MiO8etJhBK6k52u3Rj8E2031r4cKrYTf2XXsl8NnMPJWZp4B/BS4btJhBK6k5xhe06+m+GnZBKyIWRq2HgddHxExEfB9wBbBu0GIGraTmGN+Lv4/Tff/2gunMnAfIzCeBu4CH6I4UHgOeHbSYQSupMTrtzsjHEAfofsnBwtd3HV64EBHfD1yYmZuB9wAvA744aDF3HUhqjvFt79oLXBURjwJTwHURcRMwBzwAXBwRh4AzwK9n5sC7awatpOYYvptgJJnZBm5YdPpI37/ftZz1DFpJzeEDC5JUmEErSYX5UhlJKsyOVpIKG75tqxJFg/bILT9ecvmJsv7Wh6suoTZOuntbpYxp18G42dFKaoyOowNJKmw1jg4kaUX55YySVJgdrSQVNu/NMEkqy9GBJBXm6ECSynJ7lySVZkcrSYUZtJJUmI/gSlJZI3wXWCUMWknNYdBKUmHuOpCkwuxoJakwg1aSyuq0HB1IUll2tJJUltu7JKk0g1aSCqvniNagldQcnfl6Jq1BK6k56pmzBq2k5vBmmCSVZkcrSWXZ0UpSaZPY0UbEfmDNotNTQCczryhWlSSdg8581RWc3bCOdidwN/BzQE3/L0hSV02/bXxw0GbmYxHxUeCyzNy7QjVJ0rkZU9BGxDSwG9gAnAZ2ZOZc3/VfA97S+4u/Mywfh85oM/NDL6hiSVohY+xotwNrM3NTRMwCu4BtABHxYuDdwI8A64AvAAODdnpsZUlSxTrt0Y8hNgP7ADLzILCx79r/Av9ON2TXMUIfbdBKaoxOa2rkY4j1wLG+z62I6J8AfBn4EvA4cOewxQxaSY0xxo72ODDT93k6Mxc2BGwFfgh4BfByYHtE/MSgxQxaSY3RaU+NfAxxALgaoDejPdx37RvAt4DTmXkK+Cbw4kGL+cCCpMYY482wvcBVEfEo3WcHrouIm4C5zLw/In4aOBgRbeAR4G8HLWbQSmqMTmdopzqSzGwDNyw6faTv+m3AbaOuZ9BKaoyJfGBBkiZJe/hugkoYtJIaY4SbXJUwaCU1hkErSYV16vk6WoNWUnPY0UpSYePa3jVuBq2kxmi560CSyrKjlaTCnNFKUmHuOpCkwuxoJamwVrueb341aCU1hqMDSSqs7a4DSSrL7V2SVNiqHB3M7nqy5PKaUO//r/1Vl1AbR6+4pOoSGsXRgSQV5q4DSSqsppMDg1ZSczg6kKTC3HUgSYXV9EtwDVpJzdHBjlaSipp3dCBJZdnRSlJhzmglqTA7WkkqzI5Wkgpr2dFKUlk1/SYbg1ZSc7TtaCWpLF8qI0mFeTNMkgprTzk6kKSiWlUXsASDVlJjjGvXQURMA7uBDcBpYEdmzvWuvQb4cN+PzwLbM3PfUusZtJIaY4y7DrYDazNzU0TMAruAbQCZ+QXgDQAR8WbgK4NCFqCeX7AjSeegs4xjiM3APoDMPAhsXPwDEbEO+C3g3cMWM2glNUZ7avRjiPXAsb7PrYhYPAH4ZeBTmfnssMUcHUhqjDFu7zoOzPR9ns7M+UU/8wvAz4+ymB2tpMZoTY1+DHEAuBqgN6M93H8xIl4ErMnML49S17KDNiLWLPd3JGkltJdxDLEXOBURjwJ3AO+NiJsi4k2965cCT49a15Kjg4i4BrgLeB64NTP/onfpIeCnRv0DkrRSxjU6yMw2cMOi00f6rh+iuzNhJIM62luBHwMuB94VEW/rna/noxeSVr3O1OjHShp0M+xMZn4dICK2AZ+JiP+gvu9tkLTK1fVdB4M62qcj4vaIWJeZJ4BrgT8CfnRlSpOk5Wkt41hJg4L2HcAT9DrY3t21LcAnV6AuSVq2Me6jHaslRwe9PWN/uujcM8CNhWuSpHNS19GBDyxIagyDVpIKq+udeoNWUmP45YySVJgv/pakwto1HR4YtJIaw5thklRYPftZg1ZSg9jRSlJh81P17GkNWkmNUc+YNWglNYijA0kqzO1dklRYPWPWoJXUII4OJKmwVk17WoNWUmPY0UpSYR07Wkkqy45Wkgpze5ckFVbPmDVoJTXIfE2j1qCV1Bir8mbYob/7YMnlJ8q617696hJq449/YEvVJdTGzNYLqi6hUbwZJkmFrcqOVpJWkh2tJBXW6tjRSlJR7qOVpMKc0UpSYc5oJakwRweSVNi4RgcRMQ3sBjYAp4EdmTnXd30rcFvv4+PAr2bmkn98eixVSVINtDqdkY8htgNrM3MTsBPYtXAhImaADwE/m5mzwNPAhYMWM2glNUabzsjHEJuBfQCZeRDY2HftCuAwsCsi/h54JjP/Z9BiBq2kxmgv4xhiPXCs73MrIhZGrRcCW4Cbga3AjRFx6aDFDFpJjdFZxv+GOA7M9H2ezsz53r+/BhzKzK9m5kng88BrBi1m0EpqjDGODg4AVwNExCzdUcGCfwJeFREX9rrcWeBLgxZz14GkxuiM7xHcvcBVEfEoMAVcFxE3AXOZeX9E3AI83PvZT2bmFwctZtBKaoxxfd14ZraBGxadPtJ3/RPAJ0Zdz6CV1Bg+sCBJhY1xdDBWBq2kxrCjlaTCfHuXJBXmi78lqTBHB5JUmEErSYW560CSCmtERxsR3wW0MvNMoXok6ZxN5K6DiHgFcAfwVeA+YA/d14W9JzMfXIH6JGlkrU49vzVsWEd7L92va7iIbtBeCpwCHgIMWkm1Mqkz2vMz83PA5yJiS2b+N0BEzA/5PUlacZM6o82I2ANcn5lvB4iInXRHCZJUKxM5owXeCVzTe2XYgv8E7ixXkiSdm/Ykjg56AfvXi859rGhFknSOJrWjlaSJMam7DiRpYkzk6ECSJomjA0kqzI5Wkgqzo5WkwlqdVtUlnJVBK6kxJvURXEmaGJP6CK4kTQw7WkkqzF0HklSYuw4kqTAfwZWkwpzRSlJhzmglqTA7WkkqzH20klSYHa0kFeauA0kqzJthklSYowNJKmxcT4ZFxDSwG9gAnAZ2ZOZc3/U7gSuBE71T2zLz2FLrGbSSGmOMHe12YG1mboqIWWAXsK3v+muBn8nMZ0dZbHpcVUlS1dqdzsjHEJuBfQCZeRDYuHCh1+1eAnwkIg5ExDuGLVa0o13zqqumSq4/SebPHK26BKnx5s8cHVfmrAf6RwGtiDg/M+eBdcAfArcD5wH7I+IfM/OJpRazo5Wk73QcmOn7PN0LWYDngD/IzOcy8wTwGbqz3CUZtJL0nQ4AVwP0ZrSH+65dCjwSEedFxAV0xwyPD1psqq7bISSpKn27Di4DpoDr6AbvXGbeHxHvA94MPA/8eWb+yaD1DFpJKszRgSQVZtBKUmGNfWBh2JMdq1FEXA78bma+oepaqtK7eXEPcBGwBvjtzLy/0qIqEhHnAXcDAbSA6zLzqWqraqYmd7TffrID2En3yY5Vqze83wOsrbqWiv0i8LXMfB2wFbir4nqqdA1AZl4J/CbdfaEqoMlBu+STHavUU8C1VRdRA58CPtD3eX6pH2y6zPwr4Prexx8GnqmwnEZrctCe9cmOqoqpWmZ+mu5WlFUtM09m5omImAHuA95fdU1Vysz5iPgzuk863Vd1PU3V5KAd9GSHVrGIeBmwH/hoZn686nqqlplvo7sJ/+6IWFd1PU3U5KAd9GSHVqmIeCnwN8DNmXlP1fVUKSJ+KSJu6X18DmjTvSmmMWvyf0rvBa6KiEf5/yc7pN8Avhf4QEQszGq3Zua3KqypKn8J3BsRnwcuAG7MzFMV19RIPhkmSYU1eXQgSbVg0EpSYQatJBVm0EpSYQatJBVm0EpSYQatJBVm0EpSYf8HyTUZJ95+mX4AAAAASUVORK5CYII\u003d\n"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": "uniform_data1 \u003d np.random.rand(4, 4)\nprint(uniform_data1)\nsns.heatmap(data\u003duniform_data1, vmin\u003d.5, vmax\u003d1)\n",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%%\n",
          "is_executing": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "### 指定右侧的调色板的中心值, 调色板的颜色课代表正负值 \n* center 设置元素的中心值\n* 矩阵中的元素存在正负值\n* 设置调色板的中心值",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%% md\n"
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "outputs": [
        {
          "name": "stdout",
          "text": [
            "[[-0.22512748  0.37969188 -0.11569475 -0.1466201   1.76939854]\n [-0.00321878 -0.66646013 -0.77183601 -0.26496068  1.10728223]\n [-1.34873652  0.59090275  0.03795507 -0.95272402 -1.20102365]\n [-0.07904398  0.78989335  2.18617751 -0.926691    0.40184526]\n [-0.79589304  0.72023249  1.49161166  0.10117704  1.09823813]]\n"
          ],
          "output_type": "stream"
        },
        {
          "data": {
            "text/plain": "\u003cmatplotlib.axes._subplots.AxesSubplot at 0x1f20a08c278\u003e"
          },
          "metadata": {},
          "output_type": "execute_result",
          "execution_count": 57
        },
        {
          "data": {
            "text/plain": "\u003cFigure size 432x288 with 2 Axes\u003e",
            "image/png": "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\u003d\u003d\n"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": "uniform_data2 \u003d np.random.randn(5, 5) # 生成具有正负值的随机数\nprint(uniform_data2)\nsns.heatmap(data\u003duniform_data2, center\u003d0)  \n",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%%\n",
          "is_executing": false
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "outputs": [
        {
          "data": {
            "text/plain": "    year      month  passengers\n0   1949    January         112\n1   1949   February         118\n2   1949      March         132\n3   1949      April         129\n4   1949        May         121\n5   1949       June         135\n6   1949       July         148\n7   1949     August         148\n8   1949  September         136\n9   1949    October         119\n10  1949   November         104\n11  1949   December         118\n12  1950    January         115\n13  1950   February         126\n14  1950      March         141\n15  1950      April         135\n16  1950        May         125\n17  1950       June         149\n18  1950       July         170\n19  1950     August         170\n20  1950  September         158\n21  1950    October         133\n22  1950   November         114\n23  1950   December         140\n24  1951    January         145\n25  1951   February         150\n26  1951      March         178\n27  1951      April         163\n28  1951        May         172\n29  1951       June         178\n..   ...        ...         ...\n70  1954   November         203\n71  1954   December         229\n72  1955    January         242\n73  1955   February         233\n74  1955      March         267\n75  1955      April         269\n76  1955        May         270\n77  1955       June         315\n78  1955       July         364\n79  1955     August         347\n80  1955  September         312\n81  1955    October         274\n82  1955   November         237\n83  1955   December         278\n84  1956    January         284\n85  1956   February         277\n86  1956      March         317\n87  1956      April         313\n88  1956        May         318\n89  1956       June         374\n90  1956       July         413\n91  1956     August         405\n92  1956  September         355\n93  1956    October         306\n94  1956   November         271\n95  1956   December         306\n96  1957    January         315\n97  1957   February         301\n98  1957      March         356\n99  1957      April         348\n\n[100 rows x 3 columns]",
            "text/html": "\u003cdiv\u003e\n\u003cstyle scoped\u003e\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n\u003c/style\u003e\n\u003ctable border\u003d\"1\" class\u003d\"dataframe\"\u003e\n  \u003cthead\u003e\n    \u003ctr style\u003d\"text-align: right;\"\u003e\n      \u003cth\u003e\u003c/th\u003e\n      \u003cth\u003eyear\u003c/th\u003e\n      \u003cth\u003emonth\u003c/th\u003e\n      \u003cth\u003epassengers\u003c/th\u003e\n    \u003c/tr\u003e\n  \u003c/thead\u003e\n  \u003ctbody\u003e\n    \u003ctr\u003e\n      \u003cth\u003e0\u003c/th\u003e\n      \u003ctd\u003e1949\u003c/td\u003e\n      \u003ctd\u003eJanuary\u003c/td\u003e\n      \u003ctd\u003e112\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e1\u003c/th\u003e\n      \u003ctd\u003e1949\u003c/td\u003e\n      \u003ctd\u003eFebruary\u003c/td\u003e\n      \u003ctd\u003e118\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e2\u003c/th\u003e\n      \u003ctd\u003e1949\u003c/td\u003e\n      \u003ctd\u003eMarch\u003c/td\u003e\n      \u003ctd\u003e132\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e3\u003c/th\u003e\n      \u003ctd\u003e1949\u003c/td\u003e\n      \u003ctd\u003eApril\u003c/td\u003e\n      \u003ctd\u003e129\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e4\u003c/th\u003e\n      \u003ctd\u003e1949\u003c/td\u003e\n      \u003ctd\u003eMay\u003c/td\u003e\n      \u003ctd\u003e121\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e5\u003c/th\u003e\n      \u003ctd\u003e1949\u003c/td\u003e\n      \u003ctd\u003eJune\u003c/td\u003e\n      \u003ctd\u003e135\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e6\u003c/th\u003e\n      \u003ctd\u003e1949\u003c/td\u003e\n      \u003ctd\u003eJuly\u003c/td\u003e\n      \u003ctd\u003e148\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e7\u003c/th\u003e\n      \u003ctd\u003e1949\u003c/td\u003e\n      \u003ctd\u003eAugust\u003c/td\u003e\n      \u003ctd\u003e148\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e8\u003c/th\u003e\n      \u003ctd\u003e1949\u003c/td\u003e\n      \u003ctd\u003eSeptember\u003c/td\u003e\n      \u003ctd\u003e136\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e9\u003c/th\u003e\n      \u003ctd\u003e1949\u003c/td\u003e\n      \u003ctd\u003eOctober\u003c/td\u003e\n      \u003ctd\u003e119\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e10\u003c/th\u003e\n      \u003ctd\u003e1949\u003c/td\u003e\n      \u003ctd\u003eNovember\u003c/td\u003e\n      \u003ctd\u003e104\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e11\u003c/th\u003e\n      \u003ctd\u003e1949\u003c/td\u003e\n      \u003ctd\u003eDecember\u003c/td\u003e\n      \u003ctd\u003e118\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e12\u003c/th\u003e\n      \u003ctd\u003e1950\u003c/td\u003e\n      \u003ctd\u003eJanuary\u003c/td\u003e\n      \u003ctd\u003e115\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e13\u003c/th\u003e\n      \u003ctd\u003e1950\u003c/td\u003e\n      \u003ctd\u003eFebruary\u003c/td\u003e\n      \u003ctd\u003e126\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e14\u003c/th\u003e\n      \u003ctd\u003e1950\u003c/td\u003e\n      \u003ctd\u003eMarch\u003c/td\u003e\n      \u003ctd\u003e141\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e15\u003c/th\u003e\n      \u003ctd\u003e1950\u003c/td\u003e\n      \u003ctd\u003eApril\u003c/td\u003e\n      \u003ctd\u003e135\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e16\u003c/th\u003e\n      \u003ctd\u003e1950\u003c/td\u003e\n      \u003ctd\u003eMay\u003c/td\u003e\n      \u003ctd\u003e125\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e17\u003c/th\u003e\n      \u003ctd\u003e1950\u003c/td\u003e\n      \u003ctd\u003eJune\u003c/td\u003e\n      \u003ctd\u003e149\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e18\u003c/th\u003e\n      \u003ctd\u003e1950\u003c/td\u003e\n      \u003ctd\u003eJuly\u003c/td\u003e\n      \u003ctd\u003e170\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e19\u003c/th\u003e\n      \u003ctd\u003e1950\u003c/td\u003e\n      \u003ctd\u003eAugust\u003c/td\u003e\n      \u003ctd\u003e170\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e20\u003c/th\u003e\n      \u003ctd\u003e1950\u003c/td\u003e\n      \u003ctd\u003eSeptember\u003c/td\u003e\n      \u003ctd\u003e158\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e21\u003c/th\u003e\n      \u003ctd\u003e1950\u003c/td\u003e\n      \u003ctd\u003eOctober\u003c/td\u003e\n      \u003ctd\u003e133\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e22\u003c/th\u003e\n      \u003ctd\u003e1950\u003c/td\u003e\n      \u003ctd\u003eNovember\u003c/td\u003e\n      \u003ctd\u003e114\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e23\u003c/th\u003e\n      \u003ctd\u003e1950\u003c/td\u003e\n      \u003ctd\u003eDecember\u003c/td\u003e\n      \u003ctd\u003e140\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e24\u003c/th\u003e\n      \u003ctd\u003e1951\u003c/td\u003e\n      \u003ctd\u003eJanuary\u003c/td\u003e\n      \u003ctd\u003e145\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e25\u003c/th\u003e\n      \u003ctd\u003e1951\u003c/td\u003e\n      \u003ctd\u003eFebruary\u003c/td\u003e\n      \u003ctd\u003e150\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e26\u003c/th\u003e\n      \u003ctd\u003e1951\u003c/td\u003e\n      \u003ctd\u003eMarch\u003c/td\u003e\n      \u003ctd\u003e178\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e27\u003c/th\u003e\n      \u003ctd\u003e1951\u003c/td\u003e\n      \u003ctd\u003eApril\u003c/td\u003e\n      \u003ctd\u003e163\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e28\u003c/th\u003e\n      \u003ctd\u003e1951\u003c/td\u003e\n      \u003ctd\u003eMay\u003c/td\u003e\n      \u003ctd\u003e172\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e29\u003c/th\u003e\n      \u003ctd\u003e1951\u003c/td\u003e\n      \u003ctd\u003eJune\u003c/td\u003e\n      \u003ctd\u003e178\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e...\u003c/th\u003e\n      \u003ctd\u003e...\u003c/td\u003e\n      \u003ctd\u003e...\u003c/td\u003e\n      \u003ctd\u003e...\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e70\u003c/th\u003e\n      \u003ctd\u003e1954\u003c/td\u003e\n      \u003ctd\u003eNovember\u003c/td\u003e\n      \u003ctd\u003e203\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e71\u003c/th\u003e\n      \u003ctd\u003e1954\u003c/td\u003e\n      \u003ctd\u003eDecember\u003c/td\u003e\n      \u003ctd\u003e229\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e72\u003c/th\u003e\n      \u003ctd\u003e1955\u003c/td\u003e\n      \u003ctd\u003eJanuary\u003c/td\u003e\n      \u003ctd\u003e242\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e73\u003c/th\u003e\n      \u003ctd\u003e1955\u003c/td\u003e\n      \u003ctd\u003eFebruary\u003c/td\u003e\n      \u003ctd\u003e233\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e74\u003c/th\u003e\n      \u003ctd\u003e1955\u003c/td\u003e\n      \u003ctd\u003eMarch\u003c/td\u003e\n      \u003ctd\u003e267\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e75\u003c/th\u003e\n      \u003ctd\u003e1955\u003c/td\u003e\n      \u003ctd\u003eApril\u003c/td\u003e\n      \u003ctd\u003e269\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e76\u003c/th\u003e\n      \u003ctd\u003e1955\u003c/td\u003e\n      \u003ctd\u003eMay\u003c/td\u003e\n      \u003ctd\u003e270\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e77\u003c/th\u003e\n      \u003ctd\u003e1955\u003c/td\u003e\n      \u003ctd\u003eJune\u003c/td\u003e\n      \u003ctd\u003e315\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e78\u003c/th\u003e\n      \u003ctd\u003e1955\u003c/td\u003e\n      \u003ctd\u003eJuly\u003c/td\u003e\n      \u003ctd\u003e364\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e79\u003c/th\u003e\n      \u003ctd\u003e1955\u003c/td\u003e\n      \u003ctd\u003eAugust\u003c/td\u003e\n      \u003ctd\u003e347\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e80\u003c/th\u003e\n      \u003ctd\u003e1955\u003c/td\u003e\n      \u003ctd\u003eSeptember\u003c/td\u003e\n      \u003ctd\u003e312\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e81\u003c/th\u003e\n      \u003ctd\u003e1955\u003c/td\u003e\n      \u003ctd\u003eOctober\u003c/td\u003e\n      \u003ctd\u003e274\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e82\u003c/th\u003e\n      \u003ctd\u003e1955\u003c/td\u003e\n      \u003ctd\u003eNovember\u003c/td\u003e\n      \u003ctd\u003e237\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e83\u003c/th\u003e\n      \u003ctd\u003e1955\u003c/td\u003e\n      \u003ctd\u003eDecember\u003c/td\u003e\n      \u003ctd\u003e278\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e84\u003c/th\u003e\n      \u003ctd\u003e1956\u003c/td\u003e\n      \u003ctd\u003eJanuary\u003c/td\u003e\n      \u003ctd\u003e284\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e85\u003c/th\u003e\n      \u003ctd\u003e1956\u003c/td\u003e\n      \u003ctd\u003eFebruary\u003c/td\u003e\n      \u003ctd\u003e277\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e86\u003c/th\u003e\n      \u003ctd\u003e1956\u003c/td\u003e\n      \u003ctd\u003eMarch\u003c/td\u003e\n      \u003ctd\u003e317\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e87\u003c/th\u003e\n      \u003ctd\u003e1956\u003c/td\u003e\n      \u003ctd\u003eApril\u003c/td\u003e\n      \u003ctd\u003e313\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e88\u003c/th\u003e\n      \u003ctd\u003e1956\u003c/td\u003e\n      \u003ctd\u003eMay\u003c/td\u003e\n      \u003ctd\u003e318\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e89\u003c/th\u003e\n      \u003ctd\u003e1956\u003c/td\u003e\n      \u003ctd\u003eJune\u003c/td\u003e\n      \u003ctd\u003e374\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e90\u003c/th\u003e\n      \u003ctd\u003e1956\u003c/td\u003e\n      \u003ctd\u003eJuly\u003c/td\u003e\n      \u003ctd\u003e413\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e91\u003c/th\u003e\n      \u003ctd\u003e1956\u003c/td\u003e\n      \u003ctd\u003eAugust\u003c/td\u003e\n      \u003ctd\u003e405\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e92\u003c/th\u003e\n      \u003ctd\u003e1956\u003c/td\u003e\n      \u003ctd\u003eSeptember\u003c/td\u003e\n      \u003ctd\u003e355\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e93\u003c/th\u003e\n      \u003ctd\u003e1956\u003c/td\u003e\n      \u003ctd\u003eOctober\u003c/td\u003e\n      \u003ctd\u003e306\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e94\u003c/th\u003e\n      \u003ctd\u003e1956\u003c/td\u003e\n      \u003ctd\u003eNovember\u003c/td\u003e\n      \u003ctd\u003e271\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e95\u003c/th\u003e\n      \u003ctd\u003e1956\u003c/td\u003e\n      \u003ctd\u003eDecember\u003c/td\u003e\n      \u003ctd\u003e306\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e96\u003c/th\u003e\n      \u003ctd\u003e1957\u003c/td\u003e\n      \u003ctd\u003eJanuary\u003c/td\u003e\n      \u003ctd\u003e315\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e97\u003c/th\u003e\n      \u003ctd\u003e1957\u003c/td\u003e\n      \u003ctd\u003eFebruary\u003c/td\u003e\n      \u003ctd\u003e301\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e98\u003c/th\u003e\n      \u003ctd\u003e1957\u003c/td\u003e\n      \u003ctd\u003eMarch\u003c/td\u003e\n      \u003ctd\u003e356\u003c/td\u003e\n    \u003c/tr\u003e\n    \u003ctr\u003e\n      \u003cth\u003e99\u003c/th\u003e\n      \u003ctd\u003e1957\u003c/td\u003e\n      \u003ctd\u003eApril\u003c/td\u003e\n      \u003ctd\u003e348\u003c/td\u003e\n    \u003c/tr\u003e\n  \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e100 rows × 3 columns\u003c/p\u003e\n\u003c/div\u003e"
          },
          "metadata": {},
          "output_type": "execute_result",
          "execution_count": 71
        }
      ],
      "source": "flights \u003d sns.load_dataset(\"flights\") # 导入内置的航班数据\nflights.head(100)\n",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%%\n",
          "is_executing": false
        }
      }
    },
    {
      "cell_type": "markdown",
      "source": "### 使用pivot 对数据进行格式化\n* data.pivot\n* annot 矩阵元素值显示开关\n* fmt 值的显示格式\n* linewidths 格子之间的间距\n* cmp 设置调色板的色调  默认彩色 YlGnBu蓝色系",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%% md\n"
        }
      }
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "outputs": [
        {
          "data": {
            "text/plain": "\u003cmatplotlib.axes._subplots.AxesSubplot at 0x1cf48f62cf8\u003e"
          },
          "metadata": {},
          "output_type": "execute_result",
          "execution_count": 24
        },
        {
          "data": {
            "text/plain": "\u003cFigure size 432x288 with 2 Axes\u003e",
            "image/png": "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\u003d\u003d\n"
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": "sns.set_context(\"paper\") # 设置绘图的底层元素\nflights \u003d sns.load_dataset(\"flights\") # 导入内置的航班数据\nflighs1 \u003d flights.pivot(\u0027month\u0027,\u0027year\u0027,\u0027passengers\u0027) # 使用pivot对数据进行整理 按月统计\n# print(flights)\nsns.heatmap(data\u003dflighs1, annot\u003dTrue, fmt\u003d\u0027d\u0027, lw\u003d.5, cmap\u003d\u0027YlGnBu\u0027)\n",
      "metadata": {
        "pycharm": {
          "metadata": false,
          "name": "#%%\n",
          "is_executing": false
        }
      }
    }
  ],
  "metadata": {
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 2
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython2",
      "version": "2.7.6"
    },
    "kernelspec": {
      "name": "python3",
      "language": "python",
      "display_name": "Python 3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}